mCSM-AB is a novel computational method to predict the change in antibody-antigen affinity (in terms of a ∆∆G) upon the introduction
of a single mutation. It is a machine learning approach that relies on graph-based structural signatures.
Such a predictive model is not only of great relevance for antibody engineering and development, but would also allow the prediction of biologically relevant escape mutations.
To run a prediction:
Your results (1) for a single mutation will be displayed once computations are completed. The results will display the predicted
change in affinity upon mutation (ΔΔG in kcal/mol). A negative value (and red writing) corresponds
to a mutation predicted as destabilising; while a positive sign (and blue writing) corresponds to a mutation
predicted as stabilising. Complementary information also displayed include:
Your results for a list of mutations will be displayed in a table format with the following information:
In case you experience any trouble using mCSM-AB or have any suggestions or comments, please do not hesitate in contacting us (1) either via e-mail or through the online form.